A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models

A recent study delves into the complexities of optimizing equivariant neural networks, which are designed for tasks with specific symmetries. While these models show promise, the research highlights challenges in training them effectively compared to standard networks. It raises important questions about whether the constraints of equivariance hinder optimization or if there are alternative approaches that could enhance performance. Understanding these dynamics is crucial for advancing the field of machine learning and improving model efficiency.